{"id":"W3216174085","doi":"10.2139/ssrn.3927132","title":"Learning Through Social Networks: How Foreign Workers Optimize the Use of Fintech to Send Remittances","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Business; Social media; Labour economics; Economics; Computer science; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009959927,0.0002794229,0.0001981763,0.0002660308,0.0009302204,0.002282326,0.0004958038,0.001134012,0.01018432],"category_scores_gemma":[0.005245842,0.0001106501,0.0001766963,0.0002962535,0.0005271112,0.001816299,0.000938041,0.0006473875,0.0009910223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008409891,"about_ca_system_score_gemma":0.0012139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007282325,"about_ca_topic_score_gemma":0.01187342,"domain_scores_codex":[0.9997227,0.0001304929,0.000006052239,0.00004564474,0.00001993128,0.00007524024],"domain_scores_gemma":[0.997785,0.001082227,0.0003630423,0.0001257125,0.0001416705,0.000502331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.002773761,0.004654847,0.2597946,0.0004157801,0.000314641,0.00163929,0.02407394,0.1112179,0.01899925,0.07969336,0.03106223,0.4653605],"study_design_scores_gemma":[0.0006108564,0.003190211,0.2124379,0.0003139942,0.0005183053,0.0004666176,0.1152386,0.3393523,0.01280416,0.2304675,0.08432906,0.0002705219],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632965,0.0001120863,0.003134131,0.00202814,0.00003713377,0.00002031548,0.00005563616,0.00006982425,0.03124629],"genre_scores_gemma":[0.9946026,0.00004185251,0.0008651427,0.00009362576,0.000008761041,0.00000795878,0.00002659767,0.00001110374,0.004342364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01018432,"threshold_uncertainty_score":0.03406996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507283263381833,"score_gpt":0.2255840361392885,"score_spread":0.2005112035054702,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}